基于改进随机森林的故障诊断方法研究

Fault diagnosis method based on modified random forests

为解决不可识别故障诊断中无法有效定位的问题,提出一种基于改进随机森林的故障诊断方法。该方法通过改进决策树的bagging方式,采用条件概率指数进行决策树的无偏节点分裂,并以权重投票法综合决策树的分类结果。在此基础上,利用变量重要性测量来获取辅助故障定位的故障原型指数,从而较好地弥补了随机森林和传统机器学习在故障诊断中的不足和局限性。最后在一个标准数据集和田纳西-伊斯曼故障诊断的问题上进行验证,结果证明了该方法的有效性与可行性。

To solve the problem of inefficient determining fault location in unidentified fault diagnosis of traditional machine-learning technologies, a fault diagnosis method based on modified random forests was proposed. Firstly, random decision trees were created via modified algorithm of bagging and unbiased split selection based on conditional probability index so as to construct random forests. Secondly, weighted voting was applied to combine the prediction of the decision trees. Then, fault prototypes were computed through the measurement of variable-importance in random forests, which assisted in determining the fault location. Finally, the proposed method was illustrated and documented thoroughly in an application of standard dataset and Tennessee Eastman Process （TEP） fault diagnosis. The results verified the presented approach＇s feasibility and effectiveness.